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Record W2793752343 · doi:10.16995/olh.286

Counterfactual Communities: Strategy Games, Paratexts and the Player’s Experience of History

2018· article· en· W2793752343 on OpenAlexaff
Thomas Apperley

Bibliographic record

VenueOpen Library of Humanities · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Games and Media
Canadian institutionsImpact
Fundersnot available
KeywordsCounterfactual thinkingNegotiationAffordanceFraming (construction)Relation (database)Media studiesSociologyAdvertisingComputer scienceHistoryPsychologySocial scienceSocial psychology

Abstract

fetched live from OpenAlex

<p class="p1">The genre of history strategy games is a crucial area of study because of what is at stake in the representation of controversial aspects of history in popular culture. Previous work has pointed to various affordances and constraints in the representation of history, based on the framing of the game interface, the alignment of goals with certain strategies and textual criticism of the contents of the games. In contrast, this article examines these games from the perspective of the player’s experience of play in relation to a wider gaming community. It is in these counterfactual communities that players negotiate their individual experience with their knowledge of the history that is presented in the games that they play, indicating that the relationship between digital games, players and history is highly contextual. The relevant practices of players of history strategy games are illustrated with examples from the official and unofficial communities of the Paradox Interactive games <em>Europa Universalis II</em> and <em>Victoria: Empire Under the Sun</em>. The shared paratexts demonstrate how positions are negotiated in relation to the ‘official’ version of history presented in the games. These negotiations are made tangible through the production and sharing of paratexts that remix the official history of the games to include other perspectives developed through counterfactual imaginations. These findings indicate the importance of including perspectives from gaming communities to support other forms of analysis in order to make rigorous observations about the impact of digital games on popular history.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.683
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.007
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.080
GPT teacher head0.299
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations33
Published2018
Admission routes1
Has abstractyes

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